Machine learning, particularly deep learning (DL), has become a central and state-of-the-art
method for several computer vision applications and remote sensing (RS) image …

The booming development of remote sensing images in many visual tasks has led to an
increasing demand for obtaining images with more precise details. However, it is impractical …

It is observed that, in most of the CNN-based pansharpening methods, the multispectral
(MS) images are taken as the ground truth, and the downsampled panchromatic (Pan) and …

Since DCNNs (deep convolutional neural networks) have been successfully applied to
various academic and industrial fields, semantic segmentation methods, based on DCNNs …

Semantic segmentation plays a pivotal role in the intelligent interpretation of remote sensing
images (RSIs). However, conventional methods predominantly focus on learning …

Remote sensing images often contain many similar components, such as buildings, roads,
and water surfaces, which have similar spectra and spatial structures. Although …

The pan-sharpening process aims to generate a new synthetic output image preserving the
spatial details of panchromatic and spectral details of the multi-spectral image inputs …

全色图像锐化是遥感数据处理领域的一个基础性问题, 在地物分类, 目标识别等方面具有重要的
研究意义和应用价值. 近年来, 深度学习在自然语言处理, 计算机视觉等领域取得了巨大进展 …

Pansharpening is a fundamental problem in the field of remote sensing data processing. It
has important research significance and application value in ground object classification …

Deep convolutional neural networks (DCNNs) have emerged as powerful tools in diverse
remote sensing domains, but their optimization remains challenging due to their complex …